{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hX4n9TsbGw-f"
   },
   "source": [
    "##### Copyright 2018 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "0nbI5DtDGw-i"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9TnJztDZGw-n"
   },
   "source": [
    "# Text classification with an RNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AfN3bMR5Gw-o"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/alpha/tutorials/sequences/text_classification_rnn\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/r2/tutorials/sequences/text_classification_rnn.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/r2/tutorials/sequences/text_classification_rnn.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lUWearf0Gw-p"
   },
   "source": [
    "This text classification tutorial trains a [recurrent neural network](https://developers.google.com/machine-learning/glossary/#recurrent_neural_network) on the [IMDB large movie review dataset](http://ai.stanford.edu/~amaas/data/sentiment/) for sentiment analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "z682XYsrjkY9"
   },
   "outputs": [],
   "source": [
    "from __future__ import absolute_import, division, print_function\n",
    "\n",
    "!pip install tensorflow-gpu==2.0.0-alpha0\n",
    "import tensorflow_datasets as tfds\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1rXHa-w9JZhb"
   },
   "source": [
    "Import `matplotlib` and create a helper function to plot graphs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Mp1Z7P9pYRSK"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def plot_graphs(history, string):\n",
    "  plt.plot(history.history[string])\n",
    "  plt.plot(history.history['val_'+string])\n",
    "  plt.xlabel(\"Epochs\")\n",
    "  plt.ylabel(string)\n",
    "  plt.legend([string, 'val_'+string])\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pRmMubr0jrE2"
   },
   "source": [
    "## Setup input pipeline\n",
    "\n",
    "\n",
    "The IMDB large movie review dataset is a *binary classification* dataset—all the reviews have either a *positive* or *negative* sentiment.\n",
    "\n",
    "Download the dataset using [TFDS](https://www.tensorflow.org/datasets). The dataset comes with an inbuilt subword tokenizer.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SHRwRoP2nVHX"
   },
   "outputs": [],
   "source": [
    "dataset, info = tfds.load('imdb_reviews/subwords8k', with_info=True, \n",
    "                          as_supervised=True)\n",
    "train_dataset, test_dataset = dataset['train'], dataset['test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MCorLciXSDJE"
   },
   "source": [
    "As this is a subwords tokenizer, it can be passed any string and the tokenizer will tokenize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EplYp5pNnW1S"
   },
   "outputs": [],
   "source": [
    "tokenizer = info.features['text'].encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 35
    },
    "colab_type": "code",
    "id": "e7ACuHM5hFp3",
    "outputId": "81c3f892-a4d0-4218-eb32-f03669922ce9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary size: 8185\n"
     ]
    }
   ],
   "source": [
    "print ('Vocabulary size: {}'.format(tokenizer.vocab_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 53
    },
    "colab_type": "code",
    "id": "Bq6xDmf2SAs-",
    "outputId": "9e0df6ef-d01a-4f7c-f087-7b502645cf58"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tokenized string is [6307, 2327, 4043, 4265, 9, 2724, 7975]\n",
      "The original string: TensorFlow is cool.\n"
     ]
    }
   ],
   "source": [
    "sample_string = 'TensorFlow is cool.'\n",
    "\n",
    "tokenized_string = tokenizer.encode(sample_string)\n",
    "print ('Tokenized string is {}'.format(tokenized_string))\n",
    "\n",
    "original_string = tokenizer.decode(tokenized_string)\n",
    "print ('The original string: {}'.format(original_string))\n",
    "\n",
    "assert original_string == sample_string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TbhM970AVA8w"
   },
   "source": [
    "The tokenizer encodes the string by breaking it into subwords if the word is not in its dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 143
    },
    "colab_type": "code",
    "id": "GUIRWSO8yxT5",
    "outputId": "e888caaf-37e1-43f0-cae5-7726a54b336a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6307 ----> Ten\n",
      "2327 ----> sor\n",
      "4043 ----> Fl\n",
      "4265 ----> ow \n",
      "9 ----> is \n",
      "2724 ----> cool\n",
      "7975 ----> .\n"
     ]
    }
   ],
   "source": [
    "for ts in tokenized_string:\n",
    "  print ('{} ----> {}'.format(ts, tokenizer.decode([ts])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dDsCaZCDYZgm"
   },
   "outputs": [],
   "source": [
    "BUFFER_SIZE = 10000\n",
    "BATCH_SIZE = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VznrltNOnUc5"
   },
   "outputs": [],
   "source": [
    "train_dataset = train_dataset.shuffle(BUFFER_SIZE)\n",
    "train_dataset = train_dataset.padded_batch(BATCH_SIZE, train_dataset.output_shapes)\n",
    "\n",
    "test_dataset = test_dataset.padded_batch(BATCH_SIZE, test_dataset.output_shapes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bjUqGVBxGw-t"
   },
   "source": [
    "## Create the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bgs6nnSTGw-t"
   },
   "source": [
    "Build a `tf.keras.Sequential` model and start with an embedding layer. An embedding layer stores one vector per word. When called, it converts the sequences of word indices to sequences of vectors. These vectors are trainable. After training (on enough data), words with similar meanings often have similar vectors.\n",
    "\n",
    "This index-lookup is much more efficient than the equivalent operation of passing a one-hot encoded vector through a `tf.keras.layers.Dense` layer.\n",
    "\n",
    "A recurrent neural network (RNN) processes sequence input by iterating through the elements. RNNs pass the outputs from one timestep to their input—and then to the next.\n",
    "\n",
    "The `tf.keras.layers.Bidirectional` wrapper can also be used with an RNN layer. This propagates the input forward and backwards through the RNN layer and then concatenates the output. This helps the RNN to learn long range dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LwfoBkmRYcP3"
   },
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Embedding(tokenizer.vocab_size, 64),\n",
    "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)),\n",
    "    tf.keras.layers.Dense(64, activation='relu'),\n",
    "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sRI776ZcH3Tf"
   },
   "source": [
    "Compile the Keras model to configure the training process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kj2xei41YZjC"
   },
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zIwH3nto596k"
   },
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 397
    },
    "colab_type": "code",
    "id": "hw86wWS4YgR2",
    "outputId": "82d92918-8df6-4968-a19b-406ab4dfb016"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "391/391 [==============================] - 75s 191ms/step - loss: 0.5536 - accuracy: 0.7140 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/10\n",
      "391/391 [==============================] - 73s 187ms/step - loss: 0.3922 - accuracy: 0.8311 - val_loss: 0.5141 - val_accuracy: 0.7940\n",
      "Epoch 3/10\n",
      "391/391 [==============================] - 71s 182ms/step - loss: 0.3120 - accuracy: 0.8807 - val_loss: 0.4517 - val_accuracy: 0.8098\n",
      "Epoch 4/10\n",
      "391/391 [==============================] - 78s 199ms/step - loss: 0.2548 - accuracy: 0.9030 - val_loss: 0.4383 - val_accuracy: 0.8235\n",
      "Epoch 5/10\n",
      "391/391 [==============================] - 72s 185ms/step - loss: 0.2387 - accuracy: 0.9078 - val_loss: 0.4918 - val_accuracy: 0.8214\n",
      "Epoch 6/10\n",
      "391/391 [==============================] - 71s 182ms/step - loss: 0.1905 - accuracy: 0.9293 - val_loss: 0.4849 - val_accuracy: 0.8162\n",
      "Epoch 7/10\n",
      "391/391 [==============================] - 71s 182ms/step - loss: 0.1900 - accuracy: 0.9282 - val_loss: 0.5919 - val_accuracy: 0.8257\n",
      "Epoch 8/10\n",
      "391/391 [==============================] - 74s 190ms/step - loss: 0.1321 - accuracy: 0.9526 - val_loss: 0.6331 - val_accuracy: 0.7657\n",
      "Epoch 9/10\n",
      "391/391 [==============================] - 73s 187ms/step - loss: 0.3290 - accuracy: 0.8516 - val_loss: 0.6709 - val_accuracy: 0.6501\n",
      "Epoch 10/10\n",
      "391/391 [==============================] - 70s 180ms/step - loss: 0.3074 - accuracy: 0.8692 - val_loss: 0.5533 - val_accuracy: 0.7873\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_dataset, epochs=10, \n",
    "                    validation_data=test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 53
    },
    "colab_type": "code",
    "id": "BaNbXi43YgUT",
    "outputId": "621c941a-fd43-4a87-ce03-6f34b1d267eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    391/Unknown - 19s 47ms/step - loss: 0.5533 - accuracy: 0.7873Test Loss: 0.553319326714\n",
      "Test Accuracy: 0.787320017815\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_dataset)\n",
    "\n",
    "print('Test Loss: {}'.format(test_loss))\n",
    "print('Test Accuracy: {}'.format(test_acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DwSE_386uhxD"
   },
   "source": [
    "The above model does not mask the padding applied to the sequences. This can lead to skewness if we train on padded sequences and test on un-padded sequences. Ideally the model would learn to ignore the padding, but as you can see below it does have a small effect on the output.\n",
    "\n",
    "If the prediction is >= 0.5, it is positive else it is negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8w0dseJMiEUh"
   },
   "outputs": [],
   "source": [
    "def pad_to_size(vec, size):\n",
    "  zeros = [0] * (size - len(vec))\n",
    "  vec.extend(zeros)\n",
    "  return vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y-E4cgkIvmVu"
   },
   "outputs": [],
   "source": [
    "def sample_predict(sentence, pad):\n",
    "  tokenized_sample_pred_text = tokenizer.encode(sample_pred_text)\n",
    "  \n",
    "  if pad:\n",
    "    tokenized_sample_pred_text = pad_to_size(tokenized_sample_pred_text, 64)\n",
    "  \n",
    "  predictions = model.predict(tf.expand_dims(tokenized_sample_pred_text, 0))\n",
    "\n",
    "  return (predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 35
    },
    "colab_type": "code",
    "id": "O41gw3KfWHus",
    "outputId": "c843ef5e-8dcb-473b-e10b-59625d0084e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.68914342]]\n"
     ]
    }
   ],
   "source": [
    "# predict on a sample text without padding.\n",
    "\n",
    "sample_pred_text = ('The movie was cool. The animation and the graphics '\n",
    "                    'were out of this world. I would recommend this movie.')\n",
    "predictions = sample_predict(sample_pred_text, pad=False)\n",
    "print (predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 35
    },
    "colab_type": "code",
    "id": "kFh4xLARucTy",
    "outputId": "c05aa793-7a99-4837-a9e6-06b0c3be1272"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.68634349]]\n"
     ]
    }
   ],
   "source": [
    "# predict on a sample text with padding\n",
    "\n",
    "sample_pred_text = ('The movie was cool. The animation and the graphics '\n",
    "                    'were out of this world. I would recommend this movie.')\n",
    "predictions = sample_predict(sample_pred_text, pad=True)\n",
    "print (predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 297
    },
    "colab_type": "code",
    "id": "ZfIVoxiNmKBF",
    "outputId": "192273a6-aa9f-4360-e396-b886354b18ba"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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y5aFC3t+cy+P39L+hTkHTNPKKq9ibW8y+I8Ucy69AA8KC/RieHEu/7pH06tQe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\nCCFsRoqKEEIIm/lf56/V6rO3UfgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0d22c7ef90>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_graphs(history, 'accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 297
    },
    "colab_type": "code",
    "id": "IUzgkqnhmKD2",
    "outputId": "34ea7be3-9846-4c5b-d921-2417c9cf6619"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KhpY9HV68hBC/kcJRC0aDjklD2/DaJ3tYuSWdMbe30DrSLVMUBVw90bt6XvWG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\nm6wY+UMx5yMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0d198d5110>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_graphs(history, 'loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7g1evcaRpTKm"
   },
   "source": [
    "## Stack two or more LSTM layers\n",
    "\n",
    "Keras recurrent layers have two available modes that are controlled by the `return_sequences` constructor argument:\n",
    "\n",
    "* Return either the full sequences of successive outputs for each timestep (a 3D tensor of shape `(batch_size, timesteps, output_features)`).\n",
    "* Return only the last output for each input sequence (a 2D tensor of shape (batch_size, output_features))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jo1jjO3vn0jo"
   },
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Embedding(tokenizer.vocab_size, 64),\n",
    "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(\n",
    "        64, return_sequences=True)),\n",
    "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),\n",
    "    tf.keras.layers.Dense(64, activation='relu'),\n",
    "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hEPV5jVGp-is"
   },
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 397
    },
    "colab_type": "code",
    "id": "LeSE-YjdqAeN",
    "outputId": "7589ee08-d3a1-4988-f16d-a125f6960263"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "391/391 [==============================] - 155s 397ms/step - loss: 0.6349 - accuracy: 0.6162 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/10\n",
      "391/391 [==============================] - 155s 396ms/step - loss: 0.6333 - accuracy: 0.6134 - val_loss: 0.5872 - val_accuracy: 0.6914\n",
      "Epoch 3/10\n",
      "391/391 [==============================] - 153s 391ms/step - loss: 0.4199 - accuracy: 0.8217 - val_loss: 0.4361 - val_accuracy: 0.8187\n",
      "Epoch 4/10\n",
      "391/391 [==============================] - 156s 398ms/step - loss: 0.3088 - accuracy: 0.8785 - val_loss: 0.4131 - val_accuracy: 0.8319\n",
      "Epoch 5/10\n",
      "391/391 [==============================] - 153s 391ms/step - loss: 0.3328 - accuracy: 0.8564 - val_loss: 0.4689 - val_accuracy: 0.7958\n",
      "Epoch 6/10\n",
      "391/391 [==============================] - 156s 398ms/step - loss: 0.2383 - accuracy: 0.9128 - val_loss: 0.4299 - val_accuracy: 0.8404\n",
      "Epoch 7/10\n",
      "391/391 [==============================] - 152s 388ms/step - loss: 0.2426 - accuracy: 0.9039 - val_loss: 0.4934 - val_accuracy: 0.8299\n",
      "Epoch 8/10\n",
      "391/391 [==============================] - 155s 396ms/step - loss: 0.1638 - accuracy: 0.9440 - val_loss: 0.5106 - val_accuracy: 0.8279\n",
      "Epoch 9/10\n",
      "391/391 [==============================] - 150s 383ms/step - loss: 0.1616 - accuracy: 0.9420 - val_loss: 0.5287 - val_accuracy: 0.8245\n",
      "Epoch 10/10\n",
      "391/391 [==============================] - 154s 394ms/step - loss: 0.1120 - accuracy: 0.9643 - val_loss: 0.5646 - val_accuracy: 0.8070\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_dataset, epochs=10, \n",
    "                    validation_data=test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 53
    },
    "colab_type": "code",
    "id": "_LdwilM1qPM3",
    "outputId": "378205ad-03a0-44a4-be9d-172d8a88232e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    391/Unknown - 45s 115ms/step - loss: 0.5646 - accuracy: 0.8070Test Loss: 0.564571284348\n",
      "Test Accuracy: 0.80703997612\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_dataset)\n",
    "\n",
    "print('Test Loss: {}'.format(test_loss))\n",
    "print('Test Accuracy: {}'.format(test_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 35
    },
    "colab_type": "code",
    "id": "ykUKnAoqbycW",
    "outputId": "95157f33-501f-4edf-97cd-7f69384385a6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.00393916]]\n"
     ]
    }
   ],
   "source": [
    "# predict on a sample text without padding.\n",
    "\n",
    "sample_pred_text = ('The movie was not good. The animation and the graphics '\n",
    "                    'were terrible. I would not recommend this movie.')\n",
    "predictions = sample_predict(sample_pred_text, pad=False)\n",
    "print (predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 35
    },
    "colab_type": "code",
    "id": "2RiC-94zvdZO",
    "outputId": "0edf6442-873c-4785-e7a2-1ff648ee3d07"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.01098633]]\n"
     ]
    }
   ],
   "source": [
    "# predict on a sample text with padding\n",
    "\n",
    "sample_pred_text = ('The movie was not good. The animation and the graphics '\n",
    "                    'were terrible. I would not recommend this movie.')\n",
    "predictions = sample_predict(sample_pred_text, pad=True)\n",
    "print (predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 297
    },
    "colab_type": "code",
    "id": "_YYub0EDtwCu",
    "outputId": "e594edea-f660-45ae-cce9-39d9b6bc926b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jHy+zf//e6uKsqio6nY533/23l5PVn+bNQ2rdnpKSQlZWltO2adOmMX36dKdtHl1zfMqU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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0d0d8f6110>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_graphs(history, 'accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "height": 297
    },
    "colab_type": "code",
    "id": "DPV3Nn9xtwFM",
    "outputId": "be0f0418-deaa-49f2-8905-767cacdbcf1c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ewasf7+KR0R0xGWVWWyEait8sHI8//vgVt1+eR+ry9CHffPNNzScT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\nxCGEECIokjiEEEIE5S8gIKRolVxb4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0d0dfc2cd0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_graphs(history, 'loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9xvpE3BaGw_V"
   },
   "source": [
    "Check out other existing recurrent layers such as [GRU layers](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRU)."
   ]
  }
 ],
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  "colab": {
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